American Research Journal of Business and Management Original Article ISSN 2379-1047 Volume 1, Issue 3, 2015

A Framework for Studying Behaviour in the Context of Developing Customer Retention Strategies in Telecom Industry of Bihar (India)

Abhishek Singh1, Dipankar Dey2, Sukanta Chandra Swain 3

Abstract: When the number of subscribers in telecom sector in India has reached its saturation point, creating and acquiring new is not only difficult but also costly in terms of marketing parlance. Hence, the best core marketing strategy for the future is to endeavor to retain existing customers. The competition is fierce and subscribers limited, hence, the need to safeguard existing invaluable customer base. Sustainability of today’s Telecom business is majorly dependent on way the service provider handles and ring-fences its customers. Knowing your customer is at the core of the business. Thus developing customer retention strategies has been turned to be the need of hour. However, amidst cut-throat competition and saturating customer base, developing strategies to retain customers is undoubtedly a tough task. Considering the urge, we have tried to devise a framework for studying customer behavior in Indian telecom sector, particularly in the telecom zone of Bihar state, for the sake of developing customer retention strategies. The framework will facilitate research for understanding its customers and the factors in and around the customer which will trigger a specific action from him/her. It does not stop in only seeing the customer from the product and service proposition from the operator side but also understanding factors beyond like Age, Sex, Economic condition, geography, education etc. Keywords: Framework, Customer Behaviour, Retention, Strategies, Telecom Sector, Bihar

I. INTRODUCTION What we buy, how we buy, where and when we buy, in how much quantity we buy depends on our perception, self concept, social and cultural background and our age and family cycle, our attitudes, beliefs values, motivation, personality, socialclass and many other factors that are both internal and external to us. While buying, we also consider whether to buy or not to buy and, from which source or seller to buy. In some societies there is a lot of affluence and, these societies can afford to buy in greater quantities and at shorter intervals. In poor societies, the consumer can barely meet his barest needs. Management is the youngest of sciences and oldest of arts and in management is a very young discipline. Various scholars and academicians concentrated on it at a much later stage. It was during the 1950s, that marketing concept developed, and thus the need to study the behaviour of consumers was recognised. Marketing starts with the needs of the customer and ends with his satisfaction and continued association. When everything revolves round the customer, then the study of consumer behaviour becomes a necessity. It starts with the buying of goods. Goods can be bought individually, or in groups. Goods can be bought under stress (to satisfy an immediate need), for comfort and luxury in small quantities or in bulk. Indian Telecom sector has witnessed rapid growth over the last decade and is now entering the phase wherein the growth is plateauing. New subscriber additions month on month is on a declining trend (0.7%) and with the Teledensity at 72.18 (Urban 139.42 and Rural 42.43), the search for new subscribers is becoming difficult day by day.Profitability and sustained growth of a Telecom firm is possible only if it can successfully ring-fence its valuable customers.Retention of existing subscriber base is hence taking the center stage. At the core of the business is the Customer who is being influenced by a number of factors in and around him. There are factors which make a subscriber stick to a particular service provider and there are factors which triggers switch.These factors stretch much beyond the usual factors like Product, service, experience etc. and also include personal, Cultural, socio-economic, Psychological demographic factors. To be successful a service provider has to understand all those factors that influence a specific behavior of a customer: Know your Customer.

1 Research Scholar, ICFAI University Jharkhand, Ranchi, India, Email: [email protected] 2Dean, IBS Business School, Kolkata, Email: [email protected] 3Asst. Dean, ICFAI University Jharkhand, Ranchi, India, Email: [email protected] www.arjonline.org 32 American Research Journal of Business and Management, Volume 1, Issue 3, 2015 ISSN 2379-1047

II. OBJECTIVE/S AND SCOPE The Primary objectives of this research venture are; • To identify the major variables influencing a customer to switch service providers or stick to a particular service provider. • To rank the variables so identified in order of priority by assigning weightages to these variables. • To devise formulae / Index to generate a score for each customer while coming on board (This score will predict the chances of customers moving out of the system). • To devise appropriate retention strategy for ring-fencing. III. REVIEW OF LITERATURE Consumer decision making has long been of interest to researchers. Beginning about 300 years ago early economists, led by Nicholas Bernoulli, John von Neumann and Oskar Morgenstern, started to examine the basis of consumer decision making (Richarme 2007). This early work approached the topic from an economic perspective, and focused solely on the act of purchase (Loudon & DellaBitta 1993). The most prevalent model from this perspective is ‘Utility Theory’ which proposes that consumers make choices based on the expected outcomes of their decisions. Consumers are viewed as rational decision makers who are only concerned with self-interest (Schiffman&Kanuk 2007, Zinkhan 1992). Gupta. S, Lehmann. D R. and Stuart. J A, (2004) in their research paper ‘Valuing Customers’ focus on the most critical aspect of a firm: its customers. Specifically, they demonstrate how valuing customers makes it feasible to value firms, including high-growth firms with negative earnings. The authors define the value of a customer as the expected sum of discounted future earnings. They find that a 1% improvement in retention, margin, or acquisition cost improves firm value by 5%, 1%, and .1%, respectively. They demonstrate their valuation method by using publicly available data for five firms; One traditional firm (Capital One) and four Internet companies (Amazon.com, Ameritrade, eBay, and E*Trade). They found that Retention elasticity is in the range of 3 to 7 (i.e., a 1% improvement in retention increases customer value by 3%-7%). Retention rate has a significantly larger impact on customer and firm value than does the discount rate or cost of capital. It is inferred that customer lifetime value is receiving increasing attention in marketing, especially in database marketing. In this article, attempt has been made to show that the concept not only is important for tactical decisions but also can provide a useful metric to assess the overall value of a firm. The underlying premise of the model is that customers are important intangible assets of a firm, and their values should be measured and managed like any other asset. The study has been limited to a very small sample size of 5 Firms. It is difficult to quantify the value of Retention in such straight terms basis a small sample study. Gustafsson. A, Johnson. M D, and Roos. I, (2005) in their research paper titled ‘The Effects of , Relationship Commitment dimensions, and Triggers on Customer Retention’ examine the effects of customer satisfaction, affective commitment, and calculative commitment on retention. The study further examines the potential for situational and reactional trigger conditions to moderate the satisfaction -retention relationship. The results support consistent effects of customer satisfaction, calculative commitment, and prior churn on retention. Prior churn also moderates the satisfaction -retention relationship. The results have implications for both customer relationship managers and researchers who use satisfaction surveys to predict behavior. The researchers found that customer satisfaction has a consistent negative effect on churn (a positive effect on retention). In contrast, affective commitment does not predict churn when it is included with customer satisfaction. Other findings involve the effects of prior churn on future churn. Rather than rely solely on psychometric constructs to explain churn, the finding included prior churn as a state dependent variable to explain subsequent churn. It is concluded by the researchers that effective CRM strategies vary considerably depending on which factors are driving retention. If customer satisfaction is the primary driver of retention, a firm should improve product or service quality or offer better prices. If affective or calculative commitment is more important, a firm should either build more direct relationships with customers or build switching barriers in relation to competitor. A limitation of the study is that it explores only nine months of retention. Another possible limitation is that customers self-selected into the various trigger conditions using the company's own survey. However, study identified the trigger categories using qualitative inter- views from a separate sample of the company's customers. More in-depth interviews with the customers who actually responded to the survey would help ensure the more accurate prediction of the type of switching path each customer may be on.

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Research paper ‘Blinded by Delight: Why Service Fails and How to Fix It” placed in CEB, Executive Guidance for 2014, reveals that simply satisfying customers is no longer adequate; companies need to delight them, dedicating significant resources to the effort because the reward seems worth it. This unquestioning dedication of resources prompted CEB to research how quality affects customer behaviors—specifically loyalty. Key findings of this research are Service is critical to preventing customer disloyalty and that certain service experiences are far more likely to cause customer churn than others. Specifically, customers will punish organizations that require them to expend a great deal of effort to handle their service request. Ninety-six percent of customers who put forth high effort in service interactions are more disloyal, while only 9% of those with low-effort interactions are more disloyal. Organizations rarely succeed in delighting customers (only 16% of the time), and doing so is costly. It is concluded that Customers whose expectations have been exceeded are no more loyal than are those whose expectations have simply been met. Although customers seldom reward organizations that provide a delightful service experience, they will harshly penalize those that do not meet their expectations. Nguyen. N, and Leblanc. G, (2001) in their research paper ‘Corporate image and corporate reputation in customers' retention decisions in services’ presents that in the present competitive environment, corporate reputation and corporate image are acknowledged as having the potential to impact on customer loyalty toward the firm. However, the literature reveals that the precise nature of the relationships that exist between reputation and image and the understanding of their effect on customer behaviour remains a key challenge for both academia and management alike. It is found that Strong correlation between Corporate Image and reputation in ensuring customers loyalty. The results of the study reveal that the degree of customer loyalty has a tendency to be higher when perceptions of both corporate reputation and corporate image are strongly favourable. Moreover, the addition of the interaction between both constructs contributes to better explain customer loyalty. Correlation of image and reputation in Retention was established however it didn’t talk about its weightage or precedence over other factors influencing customer retention. Fornell, Claes. F, Michael D J, Eugene W A, Cha. J and Everitt. B B, (1996) in their research paper titled ‘The American Customer Satisfaction Index: Nature, Purpose, and Findings’ tried to find the American Customer Satisfaction Index (ACSI), a new type of market-based performance measure for firms, industries, economic sectors, and national economies. The authors discuss the nature and purpose of ACSI and explain the theory underlying the ACSI model, the nation-wide survey methodology used to collect the data, and the econometric approach employed to estimate the indices. Key findings of theirs are (1) customization is more important than reliability in determining customer satisfaction, (2) customer expectations play a greater role in sectors in which variance in production and consumption is relatively low, and (3) Customer satisfaction is more quality-driven than value- or price-driven. The authors find customer satisfaction to be greater for goods than for services and, in turn, greater for services than for government agencies, as well as find cause for concern in the observation that customer satisfaction in the United States is declining, primarily because of decreasing satisfaction with services. Research paper titled ‘Customer satisfaction and its consequences on customer behaviour revisited: The impact of different levels of satisfaction on word-of-mouth, feedback to the supplier and loyalty’ of Magnus Söderlund, (1998) explores the extent to which the form of the relationship between customer satisfaction and customer behaviour is different under conditions of “low” satisfaction and “high” satisfaction. Key finding of this research is that different patterns emerge for each behavioural variable. The results point to the fact that differences in the form do exist. Moreover, the results show that differences exist between the differences, in the sense that different patterns emerge for each behavioural variable. Gajjar. N B, (2013) in his research paper ‘Factors Affecting Consumer Behavior’ has studied other factors, apart from the economic factor of maximization of resource that influences customers’ behavior / buying decision. Key findings of this research are; Factors can be clubbed into: Cultural ( Culture , Subculture & Class), Social ( Reference Group , Family , Role & Status), Personal : Age , Occupation , Economic situation, Lifestyle , Personality and Psychological : Motivation , Perception , Beliefs & attitudes. It is concluded that the study of Consumer Behaviour is quite complex, because of many variables involved and their tendency to interact with & influence each other. Karjaluotoa. H, Karvonena. J, Kestia. M, Koivumäkia. T, Manninena. M, Pakolaa. J, Ristolaa. A, and Saloa. J, (2005) in their research paper titled ‘Factors Affecting Consumer Choice of Mobile Phones: Two Studies from Finland’ reveal that Mobile phone markets are one of the most turbulent market environments today due to increased competition and change. Thus, it is of growing concern to look at consumer buying decision process and cast light on the factors that finally determine consumer choices between different mobile phone brands. While technical

www.arjonline.org 34 American Research Journal of Business and Management, Volume 1, Issue 3, 2015 ISSN 2379-1047 problems are the basic reason to change mobile phone among students; price, brand, interface, and properties are the most influential factors affecting the actual choice between brands. However, their Study was confined only to student group. IV. METHODOLOGY The study will be an empirical one based on both Primary and Secondary Data. While the primary data will be collected from the customers, Customer Care and Call Centre Officials through personal interviews based on structured questionnaire, for Secondary Data Literature reviews, Telecom journals, TRAI publications and trade feedback will be referred. The study would not only cover the external factors like Product, Service, Delight but also factors intrinsic to customers viz. socioeconomic background , educational level , enculturation , sex , geography etc. The attributes to be taken care while collecting data are exhibited below. S No Personal / Consumer specific Company Specific 1 Geography Product 2 Population Price 3 Urban-Rural Service 4 Sex Brand Image 5 Age factor Differentiation 6 Literacy level 7 Religion 8 Habits and fashion In order to meet the objective of identifying the major variables influencing a customer to switch service providers and ranking the variables so identified in order of priority, the technique of Factor Analysis would be used. For devising formulae / Index to generate a score for each customer while coming on board, the technique of Discriminant Analysis would be used. To devise appropriate retention strategies for ring-fencing, if required, customers will be segregated in few categories on the basis of Cluster Analysis and strategies will be devised appropriately either factor-wise or cluster- wise. V. DATA COLLECTION AND STATISTICAL TECHNIQUES FOR ANALYSIS The study will be confined to Bihar telecom zone and segregate the telecom customers into following two Populations; a) Customers who have been loyal to one service provider for more than 2yrs, and b) Customers who have switched operator in last 6 months After stratifying each Population on the basis of location - rural and urban, gender and age-group, simple random sampling will be used to have a sample size of 500, i.e., 250 from each population. In fact, the sample units in each stratum, from the strata mentioned below will be taken in proportion to the size of the stratum. Strata Rural-Male-AgeGroup Below 18Yrs Rural-Female-Age Group Below 18 Yrs Urban-Male-Age Group Below 18Yrs Urban-Female-Age Group Below 18 Yrs Rural-Male-Age Group 18 – 25 Yrs Rural-Female-Age Group 18 – 25 Yrs Urban-Male-Age Group 18 – 25 Yrs Urban-Female-Age Group 18 – 25 Yrs

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Rural-Male-Age Group 25 - 40 Yrs Rural-Female-Age Group 25 - 40 Yrs Urban-Male-Age Group 25 - 40 Yrs Urban-Female-Age Group 25 - 40 Yrs Rural-Male-Age Group 40 - 60 Yrs Rural-Female-Age Group 40 - 60 Yrs Urban-Male-Age Group 40 - 60 Yrs Urban-Female-Age Group 40 - 60 Yrs Rural-Male-Age Group 60 Yrs and above Rural-Female-Age Group 60 Yrs and above Urban-Male-Age Group 60 Yrs and above Urban-Female-Age Group 60 Yrs and above

Strata

Stable (AON > 2Yrs) Frequent Switchers

Rural Urban Rural Urban

Male Female Male Female Male Female Male Female

Age Group: < 18, 18 – 25, 25 – 40, 40 – 60, 60 +

The statistical techniques to be used in this study are; • Factor Analysis • Discriminant Analysis • Cluster Analysis VI. CONCLUSION In order to behave rationally in the economic sense, a consumer would have to be aware of all the available consumption options, be capable of correctly rating each alternative and be available to select the optimum course of action (Schiffman & Kanuk 2007). These steps are no longer seen to be a realistic account of human decision making, as consumers rarely have adequate information, motivation or time to make such a ‘perfect’ decision and are often acted upon by less rational influences such as social relationships and values (Simon 1997). Furthermore, individuals are often described as seeking satisfactory rather than optimum choices, as highlighted by Herbert Simons Satisficing Theory (Simon 1997), or Kahneman and Tversky’s Prospect Theory (Kahneman & Tversky 1979) which embrace bounded rationality (Simon 1991). By studying customer behavior, the findings of the proposed research will help the Telecom industry ‘Know their customers better’. The Telecom operators are focusing on 360⁰ profiling of its customer base so as to make relevant / contextual retention pitch. The current limitation is that the system can study existing customer base on the basis of displayed behavior. The research result will provide a tool for the industry to predict possible churn and to provide

www.arjonline.org 36 American Research Journal of Business and Management, Volume 1, Issue 3, 2015 ISSN 2379-1047 customer profile right at the time of acquisition. This brings in a new dimension to know the customer better and also help the Operator devise timely interventions for effective Retention. This will hence provide potent tool in the hands of Industry to safeguard their customers from competition. REFERENCES

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